Vercel Deployment logo

Vercel Deployment

Community
Omar-Obando
vercel-deployment

Use when deploying applications to Vercel, configuring serverless functions, edge computing, caching strategies, and optimizing Next.js deployments. Includes Vercel CLI, Edge Functions, and performance optimization. Based on Vercel Certified Developer and modern web deployment best practices.

Overview

PublisherOmar-Obando
Repositoryqwen-orchestrator
Skill namevercel-deployment
Stars
53
Forks
13
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by Omar-Obando on GitHub. Read the source before you install it.

Installation

Install the Vercel Deployment AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Omar-Obando/qwen-orchestrator/tree/main/skills/vercel-deployment
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Omar-Obando/qwen-orchestrator.git /tmp/qwen-orchestrator
mkdir -p .claude/skills
cp -r /tmp/qwen-orchestrator/skills/vercel-deployment .claude/skills/vercel-deployment
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Vercel Deployment in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Vercel Deployment on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Vercel Deployment is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Vercel Deployment Skill — Serverless & Edge Computing

Overview

This skill provides comprehensive guidance for deploying applications to Vercel, configuring serverless functions, edge computing, caching strategies, and optimizing Next.js deployments. It includes Vercel CLI, Edge Functions, and performance optimization. Based on Vercel Certified Developer and modern web deployment best practices.

When to Use

Use this skill when:

  • Deploying Next.js applications to Vercel
  • Configuring serverless functions for Vercel Functions
  • Setting up edge computing with Vercel Edge Functions
  • Optimizing caching strategies (edge caching, static asset caching)
  • Using Vercel CLI for development (vercel dev) and deployment
  • Configuring environment variables and secrets in Vercel
  • Setting up preview deployments for pull requests
  • Configuring custom domains and SSL certificates
  • Optimizing Next.js build configurations for Vercel
  • Implementing middleware at the edge
  • Using Vercel Analytics and monitoring
  • Deploying with Git integration (GitHub, GitLab, Bitbucket)
  • Setting up automatic deployments on push
  • Configuring build environment variables

Do NOT use this skill when:

  • Writing application business logic (use developer skill or specific framework skill)
  • Designing database schema (use database-design skill)
  • Creating UI components (use frontend-design skill)
  • Managing non-Vercel cloud infrastructure (use aws-serverless, azure-cloud, or gcp-cloud skills)
  • Performing security audits (use security-auditor skill)
  • Setting up Kubernetes deployments (use devops-pipeline skill)
  • Deploying to Cloudflare Pages (use cloudflare-pages skill)
  • Developing Cloudflare Workers (use cloudflare-workers skill)

Why avoid: Vercel is specific to Next.js and Vercel infrastructure. For other platforms or infrastructure, use targeted skills.

Deployment Targets

Serverless Functions

javascript
// next.config.js
module.exports = {
  target: 'serverless',
  // Other options are still valid
};

Characteristics:

  • Minimal lambda size (~49KB)
  • Optimized cold start performance
  • No runtime dependencies
  • Self-contained build output

Edge Functions

typescript
// middleware.ts
import { edgeFunction } from '@vercel/edge';

export default edgeFunction({
  deploymentTarget: 'v8-worker',
  entrypoint: 'middleware.js',
  environment: {
    FEATURE_FLAG: 'true',
  },
  regions: 'all', // or ['iad1', 'cdg1']
});

Characteristics:

  • Runs on Cloudflare Workers runtime
  • Global distribution
  • Low latency
  • No cold starts

Vercel CLI Commands

Development

bash
# Start development server
vercel dev

# Deploy with preview
vercel --prod

# Deploy with production
vercel --prod --prod

# Deploy with environment variables
vercel --prod --env NODE_ENV=production

Deployment

bash
# Deploy to production
vercel --prod

# Deploy with custom domain
vercel --prod --prod-domain=example.com

# Deploy with build environment
vercel --prod --build-env NODE_ENV=production

Configuration

json
// vercel.json
{
  "buildCommand": "npm run build",
  "devCommand": "npm run dev",
  "installCommand": "npm install",
  "framework": "nextjs",
  "regions": ["sfo1", "iad1"],
  "headers": [
    {
      "source": "/api/(.*)",
      "headers": [
        {
          "key": "Cache-Control",
          "value": "no-store"
        }
      ]
    }
  ]
}

Performance Optimization

Caching Strategies

typescript
// API route with caching
export default async function handler(req, res) {
  res.setHeader('Cache-Control', 'public, max-age=31536000, immutable');
  res.status(200).json({ data: 'cached' });
}

Image Optimization

jsx
import Image from 'next/image';

export default function MyImage() {
  return (
    <Image
      src="/logo.png"
      alt="Logo"
      width={200}
      height={100}
      priority
      loading="eager"
    />
  );
}

Edge Caching

typescript
// Edge function with caching
export default async function request(request: Request) {
  const url = new URL(request.url);

  if (url.pathname === '/api/data') {
    return new Response(JSON.stringify({ data: 'cached' }), {
      headers: {
        'Cache-Control': 'public, max-age=86400',
      },
    });
  }

  return new Response('Not Found', { status: 404 });
}

Environment Variables

Project Settings

bash
# Set environment variable
vercel env add DATABASE_URL

# Set production environment variable
vercel env add DATABASE_URL production

# List environment variables
vercel env ls

# Remove environment variable
vercel env rm DATABASE_URL

Vercel Dashboard

  1. Navigate to project settings
  2. Go to "Environment Variables"
  3. Add variables for each environment
  4. Set "Production", "Preview", or "Development" scope

Deployment Strategies

Preview Deployments

bash
# Create preview deployment
vercel --prod

# Deploy with custom branch
vercel --prod --prod-branch=feature-branch

Production Deployments

bash
# Production deployment
vercel --prod

# Production deployment with custom domain
vercel --prod --prod-domain=example.com

Edge Function Examples

Authentication Edge Function

typescript
// middleware.ts
import { edgeFunction } from '@vercel/edge';

export default edgeFunction({
  deploymentTarget: 'v8-worker',
  entrypoint: 'middleware.js',
});

// middleware.js
export default async function handler(request) {
  const url = new URL(request.url);

  // Skip static assets
  if (url.pathname.startsWith('/_next') || url.pathname.includes('.')) {
    return null;
  }

  // Check authentication
  const token = request.headers.get('Authorization');
  if (!token && url.pathname !== '/login') {
    return new Response('Unauthorized', { status: 401 });
  }

  return null; // Continue to next handler
}

API Routing Edge Function

typescript
// api/middleware.ts
export default async function handler(request) {
  const url = new URL(request.url);

  switch (url.pathname) {
    case '/api/users':
      return handleUsers(request);
    case '/api/products':
      return handleProducts(request);
    default:
      return new Response('Not Found', { status: 404 });
  }
}

async function handleUsers(request) {
  // Handle users API
  return new Response(JSON.stringify({ users: [] }), {
    headers: { 'Content-Type': 'application/json' },
  });
}

Common Anti-Patterns

❌ Bad: Large Lambda Functions

javascript
// ❌ BAD: Large lambda with many dependencies
import { bigLibrary } from 'big-library';

export default async function handler(req, res) {
  // Heavy initialization
  const result = bigLibrary.process(req.body);
  res.json(result);
}

Problems:

  • Slow cold starts
  • High memory usage
  • Expensive execution

✅ Good: Optimized Lambda

javascript
// ✅ GOOD: Optimized lambda
export default async function handler(req, res) {
  // Lightweight processing
  const result = processRequest(req.body);
  res.json(result);
}

function processRequest(body) {
  // Simple, focused logic
  return { processed: true };
}

❌ Bad: No Caching

typescript
// ❌ BAD: No caching on static assets
export default async function handler(request) {
  const file = await readFile('/public/image.png');
  return new Response(file, {
    headers: {
      'Content-Type': 'image/png',
      // Missing Cache-Control header
    },
  });
}

✅ Good: With Caching

typescript
// ✅ GOOD: With caching
export default async function handler(request) {
  const file = await readFile('/public/image.png');
  return new Response(file, {
    headers: {
      'Content-Type': 'image/png',
      'Cache-Control': 'public, max-age=31536000, immutable',
    },
  });
}

Real-World Impact

Before this skill:

  • Slow deployments
  • Poor caching
  • High latency
  • Expensive Lambda functions

After this skill:

  • Fast deployments
  • Optimal caching
  • Low latency
  • Cost-effective functions

Cross-References

  • devops-pipeline - For CI/CD integration
  • architecture-patterns - For serverless architecture design
  • testing-strategy - For testing serverless functions

References

Frequently asked questions

What does the Vercel Deployment AI skill do?

Use when deploying applications to Vercel, configuring serverless functions, edge computing, caching strategies, and optimizing Next.js deployments. Includes Vercel CLI, Edge Functions, and performance optimization. Based on Vercel Certified Developer and modern web deployment best practices.

Why use Vercel Deployment on TypingMind?

Because you install it once and use it with any model. Vercel Deployment is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Vercel Deployment in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Omar-Obando/qwen-orchestrator/tree/main/skills/vercel-deployment. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Vercel Deployment?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Vercel Deployment?

As many as you like. As long as a model supports skills, you can use Vercel Deployment with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Vercel Deployment AI skill free?

Yes. It is published on GitHub by Omar-Obando under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇